In medical diagnostics, in particular imaging diagnostics, the possibility of multi-parametric—in particular “multimodal”—data recording is growing in importance. “Data recording” is understood here to mean the collection of medical measurement data, this referring hereinbelow mainly, but not only, to image data. Measurement data is multi-parametric with regard to different parameters. “Multimodal” here means that the data is acquired with different modalities, i.e. medical measurement devices, in particular medical imaging devices such as MRT (magnetic resonance tomograph), CT (computer tomograph), PET devices (positron emission tomograph), ultrasound systems, etc. Examples of this are multi-parametric magnetic resonance protocols, e.g. additionally in connection with a PET scan. It should be mentioned at this point that multimodal measurement data in this sense is therefore also measurement data which has been generated on combination devices, such as an MRT/PET device, i.e. that the measurement data collected using different measurement principles in a combination device is to be viewed as measurement data from different modalities.
Traditionally, the diagnosis of multi-parametric, in particular multimodal, measurement data and/or images is carried out using a variety of approaches: In one method, the various contrasts or images are displayed sequentially, i.e. the diagnostician reads the images consecutively. An example of this is the detection of a tumor as hyperintense in diffusion imaging high-b-value images and hypointense in diffusion imaging ADC images (ADC=apparent diffusion coefficient). In a further method, the contrasts are shown merged. For example, a T2 contrast of an MRT measurement can be overlaid with the PET scan, the images having previously been registered with one another for this purpose. In particular, the visualization and analysis possibilities have consequently hitherto been restricted as a rule to one-dimensional histograms and two-dimensional scatter plots. For quite specific contrast combinations or combinations of various images (hereinafter also referred to as “parameter maps”), diagnostic parameter combinations are already known, such as for example for the high b-value and the ADC value in tumor diagnoses. It is possible, therefore, to combine the values computationally in advance and then to display and/or analyze the combination value in the form of a parameter map. However, such connections are not generally known for the constantly growing number of possible contrasts. Such procedures are thus restricted to quite specific combinations of values, in principle to such cases where it is already known in advance that specific value combinations are relevant for specific diagnoses.
In clinical reality, there is consequently the risk that it is no longer possible for the increasing amount of available patient data to be analyzed adequately using traditional diagnostic methods. As a result, the potential benefits of multi-parametric imaging are possibly not always being exploited to the optimum.